Robotics 3
♻ ☆ RoboDuet: A Framework Affording Mobile-Manipulation and Cross-Embodiment
Guoping Pan, Qingwei Ben, Zhecheng Yuan, Guangqi Jiang, Yandong Ji, Jiangmiao Pang, Houde Liu, Huazhe Xu
Combining the mobility of legged robots with the manipulation skills of arms
has the potential to significantly expand the operational range and enhance the
capabilities of robotic systems in performing various mobile manipulation
tasks. Existing approaches are confined to imprecise six degrees of freedom
(DoF) manipulation and possess a limited arm workspace. In this paper, we
propose a novel framework, RoboDuet, which employs two collaborative policies
to realize locomotion and manipulation simultaneously, achieving whole-body
control through interactions between each other. Surprisingly, going beyond the
large-range pose tracking, we find that the two-policy framework may enable
cross-embodiment deployment such as using different quadrupedal robots or other
arms. Our experiments demonstrate that the policies trained through RoboDuet
can accomplish stable gaits, agile 6D end-effector pose tracking, and zero-shot
exchange of legged robots, and can be deployed in the real world to perform
various mobile manipulation tasks. Our project page with demo videos is at
https://locomanip-duet.github.io .
♻ ☆ Leveraging Symmetry in RL-based Legged Locomotion Control
Zhi Su, Xiaoyu Huang, Daniel Ordoñez-Apraez, Yunfei Li, Zhongyu Li, Qiayuan Liao, Giulio Turrisi, Massimiliano Pontil, Claudio Semini, Yi Wu, Koushil Sreenath
Model-free reinforcement learning is a promising approach for autonomously
solving challenging robotics control problems, but faces exploration difficulty
without information of the robot's kinematics and dynamics morphology. The
under-exploration of multiple modalities with symmetric states leads to
behaviors that are often unnatural and sub-optimal. This issue becomes
particularly pronounced in the context of robotic systems with morphological
symmetries, such as legged robots for which the resulting asymmetric and
aperiodic behaviors compromise performance, robustness, and transferability to
real hardware. To mitigate this challenge, we can leverage symmetry to guide
and improve the exploration in policy learning via equivariance/invariance
constraints. In this paper, we investigate the efficacy of two approaches to
incorporate symmetry: modifying the network architectures to be strictly
equivariant/invariant, and leveraging data augmentation to approximate
equivariant/invariant actor-critics. We implement the methods on challenging
loco-manipulation and bipedal locomotion tasks and compare with an
unconstrained baseline. We find that the strictly equivariant policy
consistently outperforms other methods in sample efficiency and task
performance in simulation. In addition, symmetry-incorporated approaches
exhibit better gait quality, higher robustness and can be deployed zero-shot in
real-world experiments.
♻ ★ Learning Quadruped Locomotion Using Differentiable Simulation
While most recent advancements in legged robot control have been driven by
model-free reinforcement learning, we explore the potential of differentiable
simulation. Differentiable simulation promises faster convergence and more
stable training by computing low-variant first-order gradients using the robot
model, but so far, its use for legged robot control has remained limited to
simulation. The main challenge with differentiable simulation lies in the
complex optimization landscape of robotic tasks due to discontinuities in
contact-rich environments, e.g., quadruped locomotion. This work proposes a
new, differentiable simulation framework to overcome these challenges. The key
idea involves decoupling the complex whole-body simulation, which may exhibit
discontinuities due to contact, into two separate continuous domains.
Subsequently, we align the robot state resulting from the simplified model with
a more precise, non-differentiable simulator to maintain sufficient simulation
accuracy. Our framework enables learning quadruped walking in minutes using a
single simulated robot without any parallelization. When augmented with GPU
parallelization, our approach allows the quadruped robot to master diverse
locomotion skills, including trot, pace, bound, and gallop, on challenging
terrains in minutes. Additionally, our policy achieves robust locomotion
performance in the real world zero-shot. To the best of our knowledge, this
work represents the first demonstration of using differentiable simulation for
controlling a real quadruped robot. This work provides several important
insights into using differentiable simulations for legged locomotion in the
real world.